class_docstr: Computes the forward decoding in a linear-chain CRF.
docstr: Initialize the CrfDecodeForwardRnnCell.
  
  Args:
    transition_params: A [num_tags, num_tags] matrix of binary
      potentials. This matrix is expanded into a
      [1, num_tags, num_tags] in preparation for the broadcast
      summation occurring within the cell.
docstr_tok: ["Initialize","the","CrfDecodeForwardRnnCell",".","Args",":","transition_params",":","A","[","num_tags",",","num_tags","]","matrix","of","binary","potentials",".","This","matrix","is","expanded","into","a","[","1",",","num_tags",",","num_tags","]","in","preparation","for","the","broadcast","summation","occurring","within","the","cell","."]
code: def __init__(self, transition_params: TensorLike, **kwargs):
      """Initialize the CrfDecodeForwardRnnCell.
  
      Args:
        transition_params: A [num_tags, num_tags] matrix of binary
          potentials. This matrix is expanded into a
          [1, num_tags, num_tags] in preparation for the broadcast
          summation occurring within the cell.
      """
      super().__init__(**kwargs)
      self._transition_params = tf.expand_dims(transition_params, 0)
      self._num_tags = transition_params.shape[0]
  
imports: import warnings
  import numpy
  import tensorflow
  from tensorflow_addons.rnn.abstract_rnn_cell import AbstractRNNCell
  from tensorflow_addons.utils.types import TensorLike
  from typeguard import typechecked
  from typing import Optional, Tuple
label_desc: Use image node_id 1 to create a new CrfDecodeForwardRnnCell object from inherited base classes: AbstractRNNCell with example: obj = CrfDecodeForwardRnnCell(transition_params)